Long-Term Map Maintenance in Complex Environments
Name: JOSIAS ALEXANDRE OLIVEIRA
Type: MSc dissertation
Publication date: 18/06/2021
Advisor:
Name |
Role |
|---|---|
| CLAUDINE SANTOS BADUE | Advisor * |
Examining board:
| Name |
Role |
|---|---|
| ANSELMO FRIZERA NETO | External Examiner * |
| CLAUDINE SANTOS BADUE | Advisor * |
| THIAGO OLIVEIRA DOS SANTOS | Internal Examiner * |
Summary: Autonomous vehicles should capture the external environment changes into internal
representations (for example, maps) for proper behavior and safety. As changes in external
environments are inevitable, a lifelong mapping system is desirable for autonomous robots
that rely on maps and aim at long-term operation. In this work, we propose a new large-
scale mapping system for the Intelligent Autonomous Robotic Automobile (IARA) or any
other autonomous vehicle. The new mapping system is based on the GraphSLAM algorithm,
with extensions to deal with the calibration of odometry directly in the optimization of
the graph and to address map merging for long-term map maintenance. The mapping
system can use sensor data from one or more robots to build and merge different types of
occupancy grid maps. The systems performance was evaluated in a series of experiments
carried out with data captured in complex real-world scenarios. The experimental results
indicate that the new large-scale mapping system can provide high-quality occupancy grid
maps for later navigation and localization of autonomous vehicles.
